Reprinted from: Publication of this reprint collection is supported by paid advertising SLAS Technology 27 (2022) 195–203 Contents lists available at ScienceDirect SLAS Technology journal homepage: www.elsevier.com/locate/slast Short Communication AI-driven laboratory workflows enable operation in the age of social distancing Diego Marescotti a,∗ , Chandrasekaran Narayanamoorthy b , Filipe Bonjour a , Ken Kuwae b , Luc Graber a , Florian Calvino-Martin a , Samik Ghosh b , Julia Hoeng a a PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, Neuchâtel CH-2000, Switzerland bSBX Corporation, 5-10-25 Higashi Gotanda, Shinagawa, Tokyo 141-0022, Japan a r t i c l e i n f o Keywords: Laboratory planning Machine learning a b s t r a c t The COVID-19 (Coronavirus disease 2019) global pandemic has upended the normal pace of society at multiple levels—from daily activities in personal and professional lives to the way the sciences operate. Many laboratories have reported shortage in vital supplies, change in standard operating protocols, suspension of operations because of social distancing and stay-at-home guidelines during the pandemic. This global crisis has opened opportunities to leverage internet of things, connectivity, and artificial intelligence (AI) to build a connected laboratory automation platform. However, laboratory operations involve complex, multicomponent systems. It is unrealistic to completely automate the entire diversity of laboratories and processes. Recently, AI technology, particularly, game simulation has made significant strides in modeling and learning complex, multicomponent systems. Here, we present a cloud-based laboratory management and automation platform which combines multilayer information on a simulation-driven inference engine to plan and optimize laboratory operations under various constraints of COVID-19 and risk scenarios. The platform was used to assess the execution of two cell-based assays with distinct parameters in a real-life high-content screening laboratory scenario. The results show that the platform can provide a systematic framework for assessing laboratory operation scenarios under different conditions, quantifying tradeoffs, and determining the performance impact of specific resources or constraints, thereby enabling decision-making in a cost-effective manner. We envisage the laboratory management and automation platform to be further expanded by connecting it with sensors, robotic equipment, and other components of scientific operations to provide an integrated, end-to-end platform for scientific laboratory automation. Introduction The COVID-19 global pandemic has upended the normal rhythm of society at multiple levels—from daily activities in personal and professional lives to the way businesses and the sciences operate. The disruption has affected various operations at different levels, from complete “lockdown” to “work from home” to partial or “socially distant” work procedures. The pandemic crisis has posed unprecedented challenges to the operation of scientific research laboratories across the world. Particularly, it has led to shortages in vital supplies, changes in standard operating protocols leading to suspension of operations because of social distancing and stay-at-home guidelines. These changes have led to limiting their operational capabilities causing delays and loss in productivity [1–5]. While the entire scientific community has acknowledged such socialdistancing measures as a critical step to slowing down the pandemic, ∗ Corresponding author. E-mail address: Diego.marescotti@pmi.com (D. Marescotti). these measures have come at a cost, leading to a huge “loss of scientific progress” [6–8]. Thus, the pandemic has led to a growing need for enabling laboratory operations in the “new normal” [1–3]. Despite this awareness of the challenges and costs of “shelter in place” and social distancing on laboratory research, most responses have focused on identifying remote operational issues and developing re-entry roadmaps for risk and safety compliance checklists, with little to no use of digital technologies for redefining laboratory operations [1,8]. Rapid progress in digital technologies—particularly sensing modalities for the internet-of-things, artificial intelligence (AI), and robotics—provide promising avenues for digital transformation of laboratory operations. Particularly in the area of laboratory automation, the major focus has been on robotics [3], specifically on using robotic arms to automate specific processes [[9–11]]. Similarly, electronic laboratory notes [12] and cloud-based documentation and sharing platforms [13] have enabled process automation of laboratory functions. Recent https://doi.org/10.1016/j.slast.2021.12.001 2472-6303/© 2021 The Author(s). Published by Elsevier Inc. on behalf of Society for Laboratory Automation and Screening. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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